[rocm-libraries] ROCm/rocm-libraries#6327 (commit 1e7a12e)

[CK][CK TILE] Dispatcher kernel selection heuristic for
 grouped conv (#6327)

## Motivation
The ML heuristic in dispatcher does not support grouped-conv operator
yet. In this PR, the support for fwd, bdw-data, and bwd-weight
grouped-conv kernels have been added. A tile_engine utility has also
been added to compile and run any selected kernel configuration through
dispatcher infrastructure.

## Technical Details

1. Tile engine utility is added to benchmark each shape with all the
possible kernel+tile_size combinations here -
[https://github.com/ROCm/rocm-libraries/blob/users/yraparti/ck/dispatcher-grouped-conv-heuristics/projects/composablekernel/tile_engine/ops/grouped_conv/grouped_conv_full_benchmark.py](url)
2. New LGBM regressor models for grouped conv are added to models
directory. We have 3 separate models for fwd, bwd-data, and bwd-weights
[https://github.com/ROCm/rocm-libraries/tree/users/yraparti/ck/dispatcher-grouped-conv-heuristics/projects/composablekernel/dispatcher/heuristics/models](url)
3. Implemented lazy GPU initialization (dispatcher/python)
- **Issue**: ProcessPoolExecutor fork() + GPU context caused memory
access faults
- **Solution**: Mirror FMHA pattern - defer GPU initialization until
first run()
  - **Changes**:
- setup_multiple_grouped_conv_dispatchers() returns List[Path], not
loaded libs
    - GpuGroupedConvRunner.__init__() no longer calls ctypes.CDLL
    - Added _ensure_initialized() method for lazy GPU loading
    - GPU context created only on first run() call
  - **Benefit**: Parallel compilation now works without GPU conflicts
4. Addressed few miscellaneous issues such as:
  - Fixed BF16->FP16 naming bug in the dispatcher wrapper
- Added new tile sizes, and comp_v5 pipeline to the arch spec to expand
the kernel selection
- Added automatic padding support for unsupported shapes in dispatcher
runner
- Created a single source of truth between tile_engine and dispatcher
about the architecture and tile_size details
- Build a validation scripts to compare oracle_best vs ml_heuristic
comparison

## Test Plan

1. Validated fwd, bwd-data, and bwd-weight kernels with both known and
unseen data sets with up to 300 problems.
2. Ensured that test cases are added in both dispatcher and tile_engine
to validate the heuristic.

## Test Result
Results on Unseen shapes validated on gfx950
#### Forward Pass Model
- **Training Data**: 48,845 measurements across 1,372 unique problem
shapes
- **Validation Set**: 300 unseen problems from model crawler
- **Validation Performance** (vs. oracle):
  - Mean Efficiency: **93.05%**
  - Median Efficiency: **96.8%**
  - P10 Efficiency: **79.9%**

#### Backward Data Gradient (bwd_data) Model
- **Training Data**: 18,773 measurements across 891 unique problem
shapes
- **Validation Set**: 300 unseen problems from model crawler
- **Validation Performance** (vs. oracle):
  - Mean Efficiency: **93.8%**
  - Median Efficiency: **96.5%**
  - P10 Efficiency: **82.9%**

#### Backward Weight Gradient (bwd_weight) Model
- **Training Data**: 34,900 measurements across 1,508 unique problem
shapes
- **Validation Set**: 300 unseen problems from model crawler
- **Validation Performance** (vs. oracle):
  - Mean Efficiency: **96.1%**
  - Median Efficiency: **99.2%**
  - P10 Efficiency: **89.4%**

## Submission Checklist

- [ x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.

dispatcher/examples/grouped_conv/python/02_forward.py

@@ -76,16 +76,17 @@ def main():
     print("\n--- Step 1: Declare Forward Kernels ---")
     reg = GroupedConvRegistry("forward_conv")
 
-    # Forward 2D: compv4, 128x128 tile, wave 2x2x1, warp 32x32x16
+    # Forward 2D: compv4, 64x128x64 tile (LDS 24 KiB <= 32 KiB), wave 2x2x1, warp 32x32x16
+    # Constraint: tile_m == wave_m * warp_tile_m  (small M handled by kPadM=True)
     reg.add(
         GroupedConvKernelConfig(
             variant="forward",
             ndim_spatial=2,
             arch=arch,
             dtype=args.dtype,
-            tile_m=1,
+            tile_m=64,  # = wave_m(2) * warp_tile_m(32)
             tile_n=128,
-            tile_k=128,
+            tile_k=64,
             wave_m=2,
             wave_n=2,
             wave_k=1,
@@ -99,18 +100,19 @@ def main():
             vector_size_b=8,
             vector_size_c=8,
             block_per_cu=1,
+            double_smem_buffer=True,  # required by compv4 pipeline
         )
     )
-    # Forward 3D: compv3, 64x64 tile, wave 1x4x1, warp 16x16x32
+    # Forward 3D: compv3, 16x64x128 tile, wave 1x4x1, warp 16x16x32
     reg.add(
         GroupedConvKernelConfig(
             variant="forward",
             ndim_spatial=3,
             arch=arch,
             dtype=args.dtype,
-            tile_m=1,
+            tile_m=16,  # = wave_m(1) * warp_tile_m(16)
             tile_n=64,
-            tile_k=64,
+            tile_k=128,
             wave_m=1,
             wave_n=4,
             wave_k=1,